Load Packages and Data

Daten angucken

str(death)
'data.frame':   250 obs. of  4 variables:
 $ count   : int  3 2 1 1 4 2 2 2 3 2 ...
 $ geometry: chr  "<Point><coordinates>-0.13793,51.513418</coordinates></Point>" "<Point><coordinates>-0.137883,51.513361</coordinates></Point>" "<Point><coordinates>-0.137853,51.513317</coordinates></Point>" "<Point><coordinates>-0.137812,51.513262</coordinates></Point>" ...
 $ lon     : num  -0.138 -0.138 -0.138 -0.138 -0.138 ...
 $ lat     : num  51.5 51.5 51.5 51.5 51.5 ...
str(pump)
'data.frame':   8 obs. of  4 variables:
 $ count   : int  -999 -999 -999 -999 -999 -999 -999 -999
 $ geometry: chr  "<Point><coordinates>-0.136668,51.513341</coordinates></Point>" "<Point><coordinates>-0.139586,51.513876</coordinates></Point>" "<Point><coordinates>-0.139671,51.514906</coordinates></Point>" "<Point><coordinates>-0.13163,51.512354</coordinates></Point>" ...
 $ lon     : num  -0.137 -0.14 -0.14 -0.132 -0.134 ...
 $ lat     : num  51.5 51.5 51.5 51.5 51.5 ...

Get Maps

berlin.map <- get_map("Mitte, Berlin",
                        zoom = 16, 
                        maptype = "terrain", 
                        source = "stamen")

anzeigen lassen

# mit cowplot
plot_grid(plotlist = plots, ncol = 2, labels = c("Google Maps", "Stamen Maps", "Berlin Map"))
Fehler in plot_grid(plotlist = plots, ncol = 2, labels = c("Google Maps",  : 
  Objekt 'plots' nicht gefunden

weitere Layer hinzufügen

Adressen Geokodieren

home <- geocode(location = "Birkenstr. 11, Berlin", source = "google")
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Birkenstr.%2011%2C%20Berlin

Adresse zu Map hinzufügen

Shapefiles

bb.shp <- readOGR(dsn = "data/envvk9q3g/DE_BB_Mroad_Lnight.shp", layer = "DE_BB_Mroad_Lnight")
OGR data source with driver: ESRI Shapefile 
Source: "data/envvk9q3g/DE_BB_Mroad_Lnight.shp", layer: "DE_BB_Mroad_Lnight"
with 8117 features
It has 6 fields

1.Feb Maps II

germany <- get_map(location = "Germany",
                   zoom = 6,
                   maptype = "toner",
                   source = "stamen")
Source : https://maps.googleapis.com/maps/api/staticmap?center=Germany&zoom=6&size=640x640&scale=2&maptype=terrain
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Germany
geocode failed with status OVER_QUERY_LIMIT, location = "Germany"Fehler in data.frame(ll.lat = ll[1], ll.lon = ll[2], ur.lat = ur[1], ur.lon = ur[2]) : 
  arguments imply differing number of rows: 0, 1

Have a first look

Katre laden und als kontext Layer verwenden

# Stadtnamen ergänzen
city[c(3, 5, 6), "Hauptstadt"] <- c("Berlin", "Bremen", "Hamburg")
Fehler in city[c(3, 5, 6), "Hauptstadt"] <- c("Berlin", "Bremen", "Hamburg") : 
  Objekt 'city' nicht gefunden

Daten räumlich machen

# General logic for geocoding
geocode(location = "Stuttgart", output = "latlon", source = "google", inject = ", Germany")
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Stuttgart&,%20Germany

Für viele Städte

# for loop with if-condition for warnings
# run several times until no more NA's are returned
for(i in 1:nrow(city.df)) {
  if (is.na(city.df$lon[i])) {
    result <- tryCatch(geocode(city.df$Hauptstadt[i], output = "latlon", source = "google", inject = ", Germany"),
                       warning = function(x) data.frame(lon = NA, lat = NA))
    city.df$lon[i] <- as.numeric(result[1])
    city.df$lat[i] <- as.numeric(result[2])
  }
  i <- i + 1
  Sys.sleep(sample(seq(.5, 2, 0.5), 1))  
}
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Stuttgart&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=M%C3%BCnchen&, Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Berlin&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Potsdam&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Bremen&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Hamburg&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Wiesbaden&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Schwerin&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Hannover&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=D%C3%BCsseldorf&, Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Mainz&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Saarbr%C3%BCcken&, Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Dresden&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Magdeburg&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Kiel&,%20Germany
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Erfurt&,%20Germany

Ausläneranteil darstellen

ger.map +
  geom_point(data = city.df, mapping = aes(x = lon, y = lat, size = Ausländeranteil), color = "gold2", alpha = .5)
Fehler in eval(expr, envir, enclos) : 
  Objekt 'Ausländeranteil' nicht gefunden

geht auch mit ggmap

bl.shp <- readOGR(dsn = "data/VG250_BL_2017",
                  use_iconv = TRUE, encoding = "UTF-8", stringsAsFactors = FALSE)
OGR data source with driver: ESRI Shapefile 
Source: "data/VG250_BL_2017", layer: "VG250_Bundesländergrenzen_2017"
with 35 features
It has 26 fields
Integer64 fields read as strings:  OBJECTID ADE GF BSG IBZ 

cleaning

# (2) ggfortify
bl.for <- fortify(bl.shp)
Regions defined for each Polygons

plot it

Shapefile angucken

using tmap

tm_shape(shp = bl.shp) +
  tm_polygons()

mergen

bl.shp <- sp::merge(bl.shp, city.df, by.x = "GEN", by.y = "Land")
Fehler in sp::merge(bl.shp, city.df, by.x = "GEN", by.y = "Land") : 
  Objekt 'city.df' nicht gefunden

TMap stellt daten zur Verfügung WHOOOOOP

---
title: "rvis 13"
author: "Lisa Reiber"
date: "25 1 2018"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
##Load Packages and Data
```{r, results=F, message=F, echo=FALSE}
#### install missing packages and load needed packages ####
#install.packages("devtools")
#devtools::install_github("dkahle/ggmap")
devtools::install_github("hadley/ggplot2@v2.2.0") 

if (!require("pacman")) install.packages("pacman")
pacman::p_load(sp, rgdal, ggmap, cowplot, rvest, tmap, tidyverse )      

#### load dataset ####
death <- readRDS("data/Death")
pump <- readRDS("data/Pump")
```

##Daten angucken
```{r}
str(death)
str(pump)
```

##Get Maps
```{r}
#
snow.map.ggl <- readRDS("data/snowmapggl")

#Google Map
# snow.map.ggl <- get_map(
      #             location = "London",
      #             zoom = 16,
      #             # wie groß soll der ausschnitt sein? 3 = Kontinent 21= building
      #             source = "google",
      #             maptype = "terrain"
      # )
      
# Stamen Map
snow.map.stm <- get_map("Soho, London",
                        zoom = 16, 
                        maptype = "terrain", 
                        source = "stamen")

berlin.map <- get_map("Mitte, Berlin",
                        zoom = 16, 
                        maptype = "terrain", 
                        source = "stamen")

snow.map.osm <- get_map("Soho, London",
                        zoom = 16, 
                        #maptype = "terrain", 
                        source = "osm")

bb.map <- get_map("Brandenburg",
                        zoom = 7, 
                        maptype = "terrain", 
                        source = "stamen")
ggmap(snow.map.stm)
```

## anzeigen lassen
```{r}
gg.map.ggl <- ggmap(snow.map.ggl)
gg.map.stm <- ggmap(snow.map.stm)
gg.map.brln <- ggmap(berlin.map)
gg.map.bb   <- ggmap(bb.map)

# Kombinieren und plotten (cowplot)
plots <- list(gg.map.ggl, gg.map.stm, gg.map.brln) 
plots

# CloudMade evtl interessant

# mit cowplot
plot_grid(plotlist = plots, ncol = 2, labels = c("Google Maps", "Stamen Maps", "Berlin Map"))
```

##weitere Layer hinzufügen
```{r John Snow Map}
gg.map.ggl +          # Basiskarte
  geom_point(data = death, aes(x = lon, y = lat, size = count), 
             alpha = 0.5) +   # Layer für Todesfälle
  geom_point(data = pump, aes(x = lon, y = lat), 
             color = "red", stroke = 3, shape = 4, alpha = 0.7) +   # Layer für Pumpen
  theme_void() +      # theme_void, um Achsenbeschriftung zu vermeiden
  labs(title = "Reproduction of John Snow's Cholera Map",
       size = "Cholera Deaths",
       caption = "Data: https://blog.rtwilson.com/john-snows-cholera-data-in-more-formats/")           # Titel, Caption
```
## Adressen Geokodieren
```{r}
# Query limit = 2,500 (a day)
ISW <- geocode(location = "Universitaetsstr. 3b, Berlin", source = "google")
home <- geocode(location = "Birkenstr. 11, Berlin", source = "google")
# The other way aroung
revgeocode(location = c(ISW$lon, ISW$lat), source = "google", output = "address")
revgeocode(location = c(home$lon, home$lat), source = "google", output = "address")
```
 
## Adresse zu Map hinzufügen
```{r}
gg.map.brln +          # Basiskarte
  geom_point(data = home, aes(x = lon, y = lat), 
             alpha = 0.5, stroke = 3) +   # Layer für Home
 
  theme_void() +      # theme_void, um Achsenbeschriftung zu vermeiden
  labs(title = "Who Lives Here?")           # Titel, Caption
```

## Shapefiles
```{r}
library(rgdal)
library(magrittr)
library(broom)
bb.shp <- readOGR(dsn = "data/envvk9q3g/DE_BB_Mroad_Lnight.shp", layer = "DE_BB_Mroad_Lnight")

bb.fshp <- tidy(bb.shp)
 
bb.shp@proj4string@projargs

# Plot map and overlay with shapefile
ggmap(bb.map) +
  geom_point(data = bb.fshp, mapping = aes(x = long, y = lat, group = group),
               color = "blue", fill = "black", alpha = .4)
```

## 1.Feb Maps II
```{r}
germany <- get_map(location = "Germany",
                   zoom = 6,
                   maptype = "toner",
                   source = "stamen")

germany <- readRDS("data/GermanMap")
```

Have a first look
```{r}
ger.map <- ggmap(germany)
ger.map
```

Katre laden und als kontext Layer verwenden
```{r}
# Scrapen
city <- read_html("https://de.wikipedia.org/wiki/Land_(Deutschland)") %>%
  html_nodes(css = "table.wikitable:nth-child(22)") %>%
  html_table() %>%
  .[[1]] %>%
  .[-17,-1]

# Stadtnamen ergänzen
city[c(3, 5, 6), "Hauptstadt"] <- c("Berlin", "Bremen", "Hamburg")

# Daten in numerische Werte umwandeln
city.df <- city %>%
  mutate_at(vars(`Ein-wohner(Mio.)[12]`,`Ausländer(%)[13]` ),
            .funs = funs(as.numeric(stringr::str_replace_all(string = ., pattern = ",", replacement = "\\."))))

# Variablennamen
colnames(city.df) <- c("Land", "Kürzel", "Hauptstadt", "Beitritt", "Regierung", "Parteien",
                       "Stimmen", "Fläche", "Einwohner", "EinwDichte", "Ausländeranteil", "Sprachen")


```

Daten räumlich machen
```{r}
# General logic for geocoding
geocode(location = "Stuttgart", output = "latlon", source = "google", inject = ", Germany")
```

Für viele Städte
```{r}
# Um nicht über das query limit zu kommen, nehmt lieber den Datensatz aus dem Kurs

# Wrap into a loop
# Create variables beforehand
city.df$lon <- NA
city.df$lat <- NA

# for loop with if-condition for warnings
# run several times until no more NA's are returned
for(i in 1:nrow(city.df)) {
  if (is.na(city.df$lon[i])) {
    result <- tryCatch(geocode(city.df$Hauptstadt[i], output = "latlon", source = "google", inject = ", Germany"),
                       warning = function(x) data.frame(lon = NA, lat = NA))
    city.df$lon[i] <- as.numeric(result[1])
    city.df$lat[i] <- as.numeric(result[2])
  }
  i <- i + 1
  Sys.sleep(sample(seq(.5, 2, 0.5), 1))  
}
```

Ausläneranteil darstellen 
```{r}
ger.map +
  geom_point(data = city.df, mapping = aes(x = lon, y = lat, size = Ausländeranteil), color = "gold2", alpha = .5)

```
geht auch mit ggmap
```{r}
bl.shp <- readOGR(dsn = "data/VG250_BL_2017",
                  use_iconv = TRUE, encoding = "UTF-8", stringsAsFactors = FALSE)
```
cleaning
```{r}
# Zwei Bundesländer umbennen (für später)
bl.shp@data$GEN[bl.shp@data$GEN == "Baden-Württemberg (Bodensee)"] <- "Baden-Württemberg"
bl.shp@data$GEN[bl.shp@data$GEN == "Bayern (Bodensee)"] <- "Bayern"

# (2) ggfortify
bl.for <- fortify(bl.shp)
```
 plot it
```{r}
ger.map +
  geom_polygon(data = bl.for, mapping = aes(x = long, y = lat, fill = id),
               color = "blue", fill = "black", alpha = .4)

plot(bl.shp )
```
 

Shapefile angucken
```{r}
typeof(bl.shp)

class(bl.shp)

str(bl.shp, max.level = 2) # useful args: max.level & list.len
str(bl.shp@data, max.level = 2, list.len = 3)
str(bl.shp@polygons, max.level = 2, list.len = 3)
str(bl.shp@plotOrder)
str(bl.shp@bbox)
str(bl.shp@proj4string) # http://proj4.org/projections/index.html
listviewer::jsonedit(bl.shp)
```



using tmap 
```{r}
library(tmap)
tm_shape(shp = bl.shp) +
  tm_polygons()



```
mergen
```{r}
bl.shp <- sp::merge(bl.shp, city.df, by.x = "GEN", by.y = "Land")

# Auch Seegebiete entfernen (indexing)
bl.shp <- bl.shp[bl.shp@data$GF != 1,] 

```

TMap stellt daten zur Verfügung
WHOOOOOP
```{r}
data(Europe)

glimpse(Europe@data)

tm_shape(Europe)+
      tm_polygons(col = "gdp_cap_est", style = "quantile")
```







